Gold standard evaluation of an automatic HAIs surveillance system

Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%).Xunta de Galicia | Ref. ED431C2018/55-GR

Electronically assisted surveillance systems of healthcare-associated infections:a systematic review

Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital- wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency

Electronically assisted surveillance systems of healthcare-associated infections: A systematic review

Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37 1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

BackgroundSurveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency.ObjectivesTo give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them.MethodsIn this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented.ResultsA total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved.ConclusionsElectronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency

Carbapenemase-Producing Enterobacterales

Carbapenem-resistant Enterobacterales (CRE) are a common cause of infections in both community and healthcare settings and have become an increasing threat to public health worldwide. The focus of this Special Issue includes aspects concerning plasmid-mediated antimicrobial resistance along with other carbapenem resistance mechanisms. Understanding the prevalence and routes of transmission of CRE is important in developing specific interventions for healthcare facilities, as well as the general impact of CRE circulation on the environment. Attention has also been focused on carbapenemase testing in order to provide advanced phenotypic and molecular assays for the identification of CRE, as a valid tool for active global surveillance, and from this perspective, the study of resistance mechanisms can provide significant support for the development of new and appropriate antimicrobial molecules. For all of these reasons, the phenomenon of carbapenem resistance deserves more attention, for the sake of public health

Investigations of Carbapenem-resistant Klebsiella species and associated clinical considerations

The use of many antibiotics to treat infections has become limited in the last decade. Enterobacteriaceae, especially Klebsiella spp., have acquired resistance to quinolones, aminoglycosides, cephalosporins and carbapenems. Resistance to β-lactams is mediated via extended-spectrum β-lactamases, AmpC type β-lactamases and carbapenemases combined with porin loss. Carbapenems are the antibiotics of last resort. The emergence of carbapenemase-producing organisms (CPOs) has led to Public Health England introducing a national toolkit to limit their spread. As part of this requirement, the Charing Cross microbiology laboratory of the Imperial College Healthcare NHS Trust revised its screening programme for the detection of CPOs. This improved the detection and isolation of CPOs, and highlighted Klebsiella spp. were more of a problem with respect to multidrug resistance than previously thought. Thirty-nine carbapenem-resistant Klebsiella strains were characterised. Phenotypic tests identified the strains as Klebsiella pneumoniae (n = 36) and Klebsiella oxytoca (n = 3). Detailed whole-genome sequence (WGS) analyses showed the K.oxytoca were Klebsiella michiganensis and one of the K. pneumoniae strains to be Klebsiella variicola subsp. variicola. The K. michiganensis strains were all of sequence type 138. They were predicted to encode the β-lactamases blaGES-5, blaSHV-66, blaTEM-1, blaOXA and blaCTX-M-15, and the 12- gene operon of the kleboxymycin biosynthetic gene cluster. This gene cluster encodes for tilimycin and tilivalline, enterotoxins previously thought only to be carried by K. oxytoca strains. Incorporation of antimicrobial resistance and virulence gene data showed hypervirulent, multidrug-resistant K. pneumoniae strains encoding both aerobactin and rmpA (the regulator of mucoid phenotype) or colibactin are present in West London Hospitals. These are a cause for concern, as they have the potential to cause outbreaks that are untreatable. WGS analyses yield more accurate and comprehensive data compared with phenotypic testing, enabling exact identification of clinically important strains, detailed outbreak investigations and molecular characterisation of antibiotic resistance and virulence genes in clinical settings. Thirty-two bacteriophages were isolated from sewage water and found to infect one or more of the clinical Klebsiella isolates. Some phages with broad host ranges (i.e. they infected K. pneumoniae, K. michiganensis, K. variicola and K. grimontii strains) were identified, which may have use in clinical therapeutics against multidrug-resistant infections. These bacteriophages remain to be characterised in detail

PREDICTION MODELS FOR CARBAPENEM-RESISTANT ENTEROBACTERIACEAE (CRE) AND OTHER MULTIDRUG-RESISTANT GRAM-NEGATIVE (MDRGN) BACTERIA IN HEALTHCARE SETTINGS

Background. Carbapenem-resistant Enterobacteriaceae (CRE) and other carbapenem-resistant organisms (CROs) pose urgent challenges to patient care. These bacteria are highly drug-resistant and are associated with significant attributable mortality. Current prevention strategies in United States (U.S.) healthcare facilities aim to reduce selective pressure from antibiotic exposure and to reduce patient-to-patient spread. These efforts are hampered by a lack of rapid and cost-effective diagnostics to identify these organisms. These diagnostic challenges leave basic epidemiological questions unanswered, including how many and which types of U.S. inpatients are asymptomatic carriers. Objectives. We aimed to measure the prevalence of, and risk factors for, CRO colonization among high-risk U.S. hospitalized patients and to develop statistical and machine learning prediction models that could help to address existing diagnostic limitations. Methods. To achieve these aims, we developed two study cohorts. The first, a one-year prospective cohort of Johns Hopkins Hospital (JHH) intensive care unit patients, screened patients for CRO carriage at unit admission. Isolates were speciated and molecularly characterized, and pre-admission exposure data were used to evaluate colonization risk factors and to develop predictive models of colonization with machine learning methodologies (Aim 1). The second, a retrospective cohort of JHH Gram-negative bacteremic patients, generated a clinical decision tree (Aim 2) and a risk score (Aim 3) to predict whether infections were extended-spectrum B-lactamase (ESBL)-producing. ESBLs confer resistance to most antibiotics except carbapenems, and rapid identification can reduce unnecessary carbapenem administration. Through the lens of this real-world example, we methodologically compared these two prediction approaches (Aim 3). Results. Aim 1 included 3,327 unit visits and 2,878 (87%) admission swabs. Our study found that 7.5% of patients were perirectally colonized with CROs and identified high organism and resistance mechanism diversity. Many variables were significantly associated with carriage, but resulting models were not highly predictive. Aims 2 and 3 analyzed 1,288 bacteremic patients and yielded higher performing prediction models for ESBL infection. We found that decision trees and risk scores performed similarly in our case study, but they offered different strengths and limitations. Conclusions. Statistical and machine learning prediction models offer an important complement to microbiological diagnostics. They can circumvent existing resource and practical constraints, but high biological heterogeneity can compromise their performance. Increasing familiarity with these methods, as well as refining distinctions between causal inference and prediction, may improve statistical tools for identifying colonization or infection with CROs and other multidrug-resistant bacteria